US8396716B2 - Signal compression method and apparatus - Google Patents

Signal compression method and apparatus Download PDF

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US8396716B2
US8396716B2 US12/648,994 US64899409A US8396716B2 US 8396716 B2 US8396716 B2 US 8396716B2 US 64899409 A US64899409 A US 64899409A US 8396716 B2 US8396716 B2 US 8396716B2
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US20100169086A1 (en
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Fengyan Qi
Lei Miao
Jianfeng Xu
Dejun Zhang
Herve Marcel Taddei
Qing Zhang
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Huawei Technologies Co Ltd
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/008—Multichannel audio signal coding or decoding using interchannel correlation to reduce redundancy, e.g. joint-stereo, intensity-coding or matrixing
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/08—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters
    • G10L19/12—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction

Definitions

  • the present disclosure relates to audio compression, and in particular, to a signal compression method and apparatus.
  • the speech and audio coding technologies are applied widely.
  • these coding technologies are mainly classified into lossy coding and lossless coding technologies.
  • Linear prediction (LP) analysis is widely applied in lossless compression coding to reduce the dynamic range of input signals and to remove the redundancy of the near sample points of signals, but bandwidth expansion is not generally applied in lossless coding.
  • G.729 which is a lossy coding
  • a bandwidth expansion technology is applied by multiplying the autocorrelation coefficients with a lag-window.
  • a 60 Hz bandwidth expansion is performed before calculating the LP coefficients by a Levinson-Durbin algorithm, with a view to making the LP analysis more stable.
  • the steps of calculating the LP coefficients in the prior art are as follows:
  • f s is a signal sampling frequency such as 8000 Hz
  • p is the order (such as 10) of LP analysis.
  • LP analysis is widely applied in lossless coding to reduce the dynamic range of input signals and to remove the redundancy of the near sample points of signals.
  • a signal compression method includes:
  • Another signal compression method includes:
  • a signal compression apparatus includes:
  • a windowing unit configured to multiply an input signal by a window function
  • an original autocorrelation coefficients calculating unit configured to calculate the original autocorrelation coefficients of an input signal processed by the windowing unit
  • a bandwidth expanding unit configured to adjust autocorrelation coefficient correction factors according to the original autocorrelation coefficients calculated by the original autocorrelation coefficients calculating unit, and calculate modified autocorrelation coefficients according to the original autocorrelation coefficients and the adjusted autocorrelation coefficient correction factors;
  • a linear prediction coefficients calculating unit configured to calculate the linear prediction coefficients according to the modified autocorrelation coefficients calculated by the bandwidth expanding unit
  • a compressing unit configured to code the input signal according to the linear prediction coefficients calculated by the linear prediction coefficients calculating unit, and output a coded bit stream.
  • the autocorrelation coefficient correction factors are adjusted according to the original autocorrelation coefficients so that the adjusted autocorrelation coefficient correction factors can express the difference of input signals, thereby avoiding ill-conditioned cases of special input signals, making the modified autocorrelation coefficients more suitable for subsequent compression processing, improving the compression efficiency of a lossless coder and the quality of reconstructed speech signals of a lossy coder, and involving only simple operations.
  • FIG. 1 is a flowchart of a signal compression method in the first embodiment of the present disclosure
  • FIG. 2 is a flowchart of a signal compression method in the second embodiment of the present disclosure
  • FIG. 3 is a flowchart of a signal compression method in the third embodiment of the present disclosure.
  • FIG. 4 is a flowchart of a signal compression method in the fourth embodiment of the present disclosure.
  • FIG. 5 shows a structure of a signal compression apparatus in the fifth embodiment of the present disclosure
  • FIG. 6 shows a structure of a signal compression apparatus in the sixth embodiment of the present disclosure
  • FIG. 7 shows a structure of a bandwidth expanding unit of a signal compression apparatus in the sixth embodiment of the present disclosure
  • FIG. 8 shows a structure of a signal compression apparatus in the seventh embodiment of the present disclosure
  • FIG. 9 shows a structure of a bandwidth expanding unit of a signal compression apparatus in the seventh embodiment of the present disclosure.
  • FIG. 10 shows another structure of a bandwidth expanding unit in the sixth or seventh embodiment of the present disclosure.
  • FIG. 1 is a flowchart of a signal compression method in the first embodiment of the present disclosure. The method includes the following steps:
  • Step 101 Multiply an input signal by a window function.
  • Step 102 Calculate original autocorrelation coefficients of a windowed input signal.
  • Step 103 Adjust autocorrelation coefficient correction factors according to the original autocorrelation coefficients.
  • Step 104 Calculate modified autocorrelation coefficients according to the original autocorrelation coefficients and the adjusted autocorrelation coefficient correction factors.
  • the autocorrelation coefficient correction factors include a white-noise correction factor and a lag-window. Adjusting the autocorrelation coefficient correction factors may be: adjusting the white-noise correction factor and the lag-window, or adjusting the white-noise correction factor only, or adjusting the lag-window only.
  • Adjusting the autocorrelation coefficient correction factors according to the original autocorrelation coefficients may be: determining characteristic parameters of the input signal according to the original autocorrelation coefficients and adjusting the autocorrelation coefficient correction factors according to the characteristic parameters.
  • the characteristic parameters may be: energy, periodicity parameter, zero crossing rate, or reflection coefficient, or any combination thereof; and may be extracted from original input signals or signals obtained in any step.
  • Step 105 Calculate LP coefficients according to the modified autocorrelation coefficients.
  • Step 106 Code the input signal according to the LP coefficients, and output a coded bit stream.
  • Coding the input signal according to the LP coefficients may be: performing LP analysis for the input signal according to the LP coefficients, calculating a residual signal, and then performing Long Term Prediction (LTP) and entropy coding, and finally, outputting a lossless coded bit stream of the residual signal; or, inputting the LP coefficients and the input signal into the Code Excited Linear Prediction (CELP) model to obtain the bit stream.
  • LTP Long Term Prediction
  • CELP Code Excited Linear Prediction
  • a preprocessing step may be included. Before step 101 , the input signal is preprocessed.
  • the preprocessing may be a pre-emphasis filtering or a high-pass filtering for increasing the high-frequency components of the input signal or filtering out unnecessary low-frequency interference components.
  • the filtered signal is windowed according to step 101 .
  • the preprocessing may be a mapping operation; that is, the input signal is mapped from the A-law or ⁇ -law to the Pulse Coding Modulation (PCM) domain. The signals in the PCM domain are more suitable for LP short-term prediction.
  • PCM Pulse Coding Modulation
  • the original autocorrelation coefficients reflect the characteristics of each frame signal; according to such characteristics, the autocorrelation coefficient correction factors are adjusted so that the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 2 is a flowchart of a signal compression method in the second embodiment of the present disclosure. The method includes the following steps:
  • Step 201 Multiply an input signal by a window function.
  • the window here may be the window applied to lossy coding in the prior art.
  • Step 202 Calculate original autocorrelation coefficients r(k) ac cording to the windowed input signal s′ (n), for example, through the following formula:
  • Step 203 Calculate an energy parameter E according to the original autocorrelation coefficients.
  • Step 204 Adjust a white-noise correction factor according to the energy parameter.
  • an energy threshold E thr may be set. According to the relationship between the energy parameter E and the E thr , the input signals are differentiated. Different adjustment functions are used to adjust the white-noise correction factor for different input signals. Specifically, different adjustment functions are used to adjust the white-noise correction factor according to different energy threshold intervals in which the energy parameter ranges according to the following equation:
  • the energy threshold E thr is determined as a constant that can differentiate between unvoiced and voiced speech by plenty of speech corpora. Considering the impact from the frame length, different energy thresholds may be set for different frame lengths, for example,
  • Step 205 Calculate a lag-window win lag (k) according to an expansion bandwidth f 0 :
  • f 0 is the expansion bandwidth such as 34 Hz
  • f s is a signal sampling frequency such as 8000 Hz
  • p is the order of LP.
  • Step 207 Use the modified autocorrelation coefficients r(0)′ . . . r(k)′ to calculate LP coefficients through a Levinson-Durbin algorithm.
  • Step 208 Code the input signal according to the LP coefficients, and output a coded bit stream.
  • Performing the compression coding for the input signal according to the LP coefficients may be: calculating a residual signal of the input signal through LP analysis, and then performing LTP and entropy coding, and finally, outputting a lossless coded bit stream of the residual signal; or, inputting the LP coefficients and the input signal into the CELP model to obtain a coded bit stream.
  • the energy parameter that indicates the characteristics of the input signal is calculated through the original autocorrelation coefficients; according to the energy parameter, the white-noise correction factor is adjusted so that the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 3 is a flowchart of a signal compression method in the third embodiment of the present disclosure. The method includes the following steps:
  • Step 301 Multiply an input signal by a window function.
  • the window here may be the window applied to lossy coding in the prior art.
  • Step 302 Calculate the original autocorrelation coefficients r(k) according to the windowed input signal s′(n), for example, through the following formula:
  • Step 304 Calculate at least one reflection coefficient of the windowed input signal according to the original autocorrelation coefficients.
  • the reflection coefficient may be calculated through the Levinson-Durbin recursive algorithm:
  • Step 305 According to the at least one reflection coefficient, for example k 1 , adaptively calculate and adjust an expansion bandwidth f 0 :
  • F may be a constant such as 60 Hz
  • Step 306 Calculate a lag-window according to the expansion bandwidth f 0 :
  • f 0 is the expansion bandwidth calculated in step 305 ;
  • f s is a signal sampling frequency such as 8000 Hz; and
  • p is the order of LP.
  • Step 308 Use the modified autocorrelation coefficients r(0)′ . . . r(k)′ to calculate LP coefficients through a Levinson-Durbin algorithm.
  • Step 309 Code the input signal according to the LP coefficients, and output a coded bit stream. Coding the input signal according to the LP coefficients may be: inputting the LP coefficients and the input signal into the CELP model to obtain a coded bit stream; or, calculating a residual signal of the input signal through LP analysis, and then performing LTP and entropy coding, and finally, outputting a lossless coded bit stream of the residual signal.
  • the reflection coefficient that indicates the characteristics of the input signal is calculated according to the original autocorrelation coefficients.
  • the expansion bandwidth is determined according to the reflection coefficient, and the lag-window is adjusted according to the determined expansion bandwidth.
  • the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 4 is a flowchart of a signal compression method in the fourth embodiment of the present disclosure. The method includes the following steps:
  • Step 401 Multiply an input signal by a window function.
  • the window here may be the window applied to lossy coding in the prior art.
  • Step 402 Calculate original autocorrelation coefficients r(k) ac cording to the windowed input signal s′(n), for example, through the following formula:
  • Step 403 Calculate an energy parameter according to the original autocorrelation coefficients.
  • Step 404 Adjust a white-noise correction factor according to the energy parameter.
  • an energy threshold E thr may be set. According to the relationship between the energy parameter E and the E thr , the input signals are differentiated. Different adjustment functions are used to adjust the white-noise correction factor for different input signals. Specifically, different adjustment functions are used to adjust the white-noise correction factor according to different energy threshold intervals that in which the energy parameter ranges:
  • E thr , H, L, ⁇ , ⁇ are empirical constants, which may be obtained according to the specific conditions.
  • E thr , H, L, ⁇ , ⁇ are empirical constants that may be obtained according to the specific conditions.
  • Step 405 Calculate at least one reflection coefficient of the windowed input signal according to the original autocorrelation coefficients.
  • the first reflection coefficient is calculated to simplify the calculation, but the present disclosure is not limited to calculate only the first reflection coefficient.
  • k 1 r (1)/ r (0).
  • Step 406 According to the at least one reflection coefficient, for example k 1 , adaptively calculate and adjust an expansion bandwidth f 0 :
  • F may be a constant such as 60 Hz
  • Step 407 Calculate a lag-window according to the expansion bandwidth f 0 :
  • f 0 is the expansion bandwidth calculated in step 406 ;
  • f s is a signal sampling frequency such as 8000 Hz; and
  • p is the order of LP.
  • Step 409 Use the modified autocorrelation coefficients r(0)′ . . . r(k)′ to calculate LP coefficients through a Levinson-Durbin algorithm.
  • Step 410 Code the input signal according to the LP coefficients, and output a coded bit stream.
  • Coding the input signal according to the LP coefficients may be: calculating a residual signal of the input signal through LP analysis, and then performing LTP and entropy coding, and finally, outputting a lossless coded bit stream of the residual signal; or, inputting the LP coefficients and the input signal into the CELP model to obtain a coded bit stream.
  • the energy parameter and the reflection coefficient that indicates the characteristics of the input signal are calculated according to the original autocorrelation coefficients.
  • the white-noise correction factor is adjusted according to the energy parameter.
  • the expansion bandwidth is determined according to the reflection coefficient, and the lag-window is adjusted according to the determined expansion bandwidth.
  • the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 5 shows a structure of a signal compression apparatus in the fifth embodiment of the present disclosure.
  • the apparatus includes:
  • a windowing unit 501 configured to multiply an input signal by a window function
  • an original autocorrelation coefficients calculating unit 502 configured to calculate the original autocorrelation coefficients of an input signal processed by the windowing unit 501 ;
  • a bandwidth expanding unit 503 configured to adjust autocorrelation coefficient correction factors according to the original autocorrelation coefficients calculated by the original autocorrelation coefficients calculating unit 502 , and calculate modified autocorrelation coefficients according to the original autocorrelation coefficients and the adjusted autocorrelation coefficient correction factors;
  • a linear prediction coefficients calculating unit 504 configured to calculate the LP coefficients according to the modified autocorrelation coefficients calculated by the bandwidth expanding unit 503 ;
  • a compressing unit 505 configured to code the input signal according to the LP coefficients calculated by the linear prediction coefficients calculating unit 504 , and output a coded bit stream.
  • the apparatus may further include a preprocessing unit 500 , which is configured to preprocess the input signal for different types of compression, and send a preprocessed input signal to the windowing unit 501 to make the input signal more suitable for being processed by subsequent modules.
  • the preprocessing unit may be a pre-emphasis filtering or a high-pass filter which is configured to increase the high-frequency components of the input signal or to filter out unnecessary low-frequency interference components. Afterward, the filtered signal is input into the windowing unit 501 .
  • the preprocessing unit may be a mapping module which maps the input signal from the A-law or ⁇ -law to the PCM domain. The signals in the PCM domain are more suitable for LP short-term prediction.
  • the original autocorrelation coefficients reflect the characteristics of each frame signal; according to such characteristics, the autocorrelation coefficient correction factors are adjusted so that the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 6 shows a structure of a signal compression apparatus in the sixth embodiment of the present disclosure.
  • the apparatus includes: a windowing unit 601 , an original autocorrelation coefficients calculating unit 602 , a bandwidth expanding unit 603 , an LP coefficients calculating unit 604 , an LP predicting unit 605 , an LTP processing unit 606 , and an entropy coding unit 607 .
  • the windowing unit 601 is configured to multiply an input signal by a window function.
  • the windowing unit 601 may be a windowing unit applied to lossy coding in the prior art.
  • the original autocorrelation coefficients calculating unit 602 is configured to calculate the original autocorrelation coefficients of an input signal processed by the windowing unit 601 , for example, through the following formula:
  • the bandwidth expanding unit 603 may include an energy module 701 , a white-noise correction factor module 702 , a lag-window module 703 , and a modified autocorrelation coefficients calculating module 704 .
  • the energy module 701 is configured to calculate an energy parameter according to the original autocorrelation coefficients.
  • the white-noise correction factor module 702 is configured to adjust the white-noise correction factor according to the energy parameter calculated by the energy module 701 .
  • an energy threshold E thr may be set. According to the relationship between the energy parameter E and the E thr , the input signals are differentiated. Different adjustment functions are used to adjust the white-noise correction factor for different input signals. Specifically, different adjustment functions are used to adjust the white-noise correction factor according to different energy threshold intervals in which the energy parameter ranges:
  • E thr , H, L, ⁇ , ⁇ are empirical constants, which may be obtained according to the specific conditions.
  • H, L, ⁇ , ⁇ are empirical constants, which may be obtained according to the specific conditions.
  • the lag-window module 703 is configured to calculate a lag-window win lag (k) according to an expansion bandwidth f 0 :
  • f 0 is the expansion bandwidth such as 34 Hz
  • f s is a signal sampling frequency such as 8000 Hz
  • p is the order of LP.
  • the LP coefficients calculating unit 604 is configured to calculate the LP coefficients through the Levinson-Durbin algorithm according to the modified autocorrelation coefficients r(0)′ . . . r(k)′ adjusted by the bandwidth expanding unit 603 .
  • the LP predicting unit 605 is configured to perform LP analysis for the input signal according to the LP coefficients calculated by the LP coefficients calculating unit 604 , and calculate a residual signal.
  • the LTP processing unit 606 is configured to perform LTP for the residual signal output by the LP predicting unit 605 .
  • the entropy coding unit 607 is configured to perform entropy coding for the signal which are output by the LTP processing unit 606 after the long-term prediction, and output the lossless coded bit stream of the residual signal.
  • the LP predicting unit 605 , the LTP processing unit 606 , and the entropy coding unit 607 may be the functional units applied in the prior art.
  • the energy parameter that indicates the characteristics of the input signal is calculated according to the original autocorrelation coefficients.
  • the white-noise correction factor is adjusted according to the energy parameter so that the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • FIG. 8 shows a structure of a signal compression apparatus in the seventh embodiment of the present disclosure.
  • the apparatus includes: a windowing unit 801 , an original autocorrelation coefficients calculating unit 802 , a bandwidth expanding unit 803 , an LP coefficients calculating unit 804 , and a CELP coding unit 805 .
  • the windowing unit 801 is configured to multiply an input signal by a window function.
  • the windowing unit 801 may be a windowing unit applied to lossy coding in the prior art.
  • the original autocorrelation coefficients calculating unit 802 is configured to calculate the original autocorrelation coefficients of an input signal processed by the windowing unit 801 , for example, through the following formula:
  • the bandwidth expanding unit 803 may include a white-noise correction factor module 901 , a reflection coefficient calculating module 902 , an expansion bandwidth calculating module 903 , a lag-window module 904 , and a modified autocorrelation coefficients calculating module 905 .
  • the expansion bandwidth calculating module 903 is configured to adaptively calculate and adjust the expansion bandwidth according to the reflection coefficient k 1 calculated by the reflection coefficient calculating module 902 :
  • f 0 F+ ⁇ k 1 , where F may be 60 Hz, and ⁇ is an empirical factor which is determined experimentally.
  • the lag-window module 904 is configured to calculate the lag-window according to the expansion bandwidth f 0 output by the expansion bandwidth calculating module 903 :
  • the LP coefficients calculating unit 804 is configured to calculate the LP coefficients through the Levinson-Durbin algorithm according to the modified autocorrelation coefficients r(0)′ . . . r(k)′ adjusted by the bandwidth expanding unit 803 .
  • the CELP coding unit 805 is configured to input the LP coefficients calculated by the LP coefficients calculating unit 804 and the input signal into the CELP model to obtain a coded bit stream.
  • the bandwidth expanding unit in another embodiment may include an energy module 1001 , a white-noise correction factor module 1002 , a reflection coefficient calculating module 1003 , an expansion bandwidth calculating module 1004 , a lag-window module 1005 , and a modified autocorrelation coefficients calculating module 1006 .
  • the bandwidth expanding unit shown in FIG. 10 may be an alternative of the bandwidth expanding unit 603 in the sixth embodiment and the bandwidth expanding unit 803 in the seventh embodiment; the bandwidth expanding unit 603 may be applied in the seventh embodiment to replace the bandwidth expanding unit 803 , and the bandwidth expanding unit 803 may be applied in the sixth embodiment to replace the bandwidth expanding unit 603 .
  • the energy module 1001 is configured to calculate an energy parameter according to the original autocorrelation coefficients.
  • the white-noise correction factor module 1002 is configured to adjust the white-noise correction factor according to the energy parameter calculated by the energy module 1001 .
  • an energy threshold E thr may be set. According to the relationship between the energy parameter E and the E thr , the input signals are differentiated. Different adjustment functions are used to adjust the white-noise correction factor for different input signals. Specifically, different adjustment functions are used to adjust the white-noise correction factor according to different energy threshold intervals in which the energy parameter ranges:
  • E thr , H, L, ⁇ , ⁇ are empirical constants, which may be obtained according to the specific conditions.
  • H, L, ⁇ , ⁇ are empirical constants, which may be obtained according to the specific conditions.
  • the reflection coefficient calculating module 1003 is configured to calculate at least one reflection coefficient of the frame signal according to the original autocorrelation coefficients. In this embodiment, only the first reflection coefficient is calculated to simplify the calculation, but the present disclosure is not limited to calculate only the first reflection coefficient.
  • k 1 r (1)/ r (0).
  • the expansion bandwidth calculating module 1004 is configured to adaptively calculate and adjust the expansion bandwidth according to the reflection coefficient k 1 calculated by the reflection coefficient calculating module 1003 :
  • f 0 F+ ⁇ k 1 , where F may be 60 Hz, and ⁇ is an empirical factor which is determined experimentally.
  • the lag-window module 1005 is configured to calculate the lag-window according to the expansion bandwidth f 0 output by the expansion bandwidth calculating module 1004 :
  • f 0 is the expansion bandwidth calculated by the expansion bandwidth calculating module 1004 ;
  • f s is a signal sampling frequency such as 8000 Hz; and
  • p is the order of LP.
  • the energy parameter and the reflection coefficient that indicates the characteristics of the input signal are calculated according to the original autocorrelation coefficients, the white-noise correction factor is adjusted according to the energy parameter.
  • the expansion bandwidth is determined according to the reflection coefficient, and the lag-window is adjusted according to the determined expansion bandwidth.
  • the adjusted autocorrelation coefficient correction factors are determined according to the characteristics of each frame signal. Therefore, the LP coefficients fit in with the characteristics of the signals more accurately; ill-conditioned cases are avoided; the calculated coefficients are more robust; and the calculation complexity is low.
  • the LP coefficients are calculated according to the modified autocorrelation coefficients through many algorithms such as the Levinson-Durbin algorithm, covariance method, and lattice method.
  • the foregoing embodiments take the Levinson-Durbin algorithm as an example, but the present disclosure does not limit the algorithm.
  • multiple reflection coefficients k i of the windowed input signal may be calculated according to the original autocorrelation coefficients, and then the expansion bandwidth is calculated through one or more reflection coefficients.
  • the calculation mode of the expansion bandwidth may change accordingly. That is, multiple reflection coefficients are used together with multiple regulating expansion factors to generate a new expression between the reflection coefficient and the expansion bandwidth.
  • the embodiments of the present disclosure give an exemplary expression between the reflection coefficient and the expansion bandwidth, but those skilled in the art may derive various expressions between the reflection coefficient and the expansion bandwidth from the embodiments described herein without creative work.
  • the present disclosure does not limit the expression between the reflection coefficient and the expansion bandwidth.
  • the regulating expansion factor corresponding to each reflection coefficient may be obtained through training by using representative training data, and the training is benchmarked against the final coder performance, and then various expressions between the reflection coefficient and the expansion bandwidth are constructed.
  • the program may be stored in a computer-readable storage medium. When being executed, the program performs the processes covered in the foregoing embodiments.
  • the storage medium may be a magnetic disk, a compact disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

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US20130339009A1 (en) * 2011-01-14 2013-12-19 Panasonic Corporation Coding device, communication processing device, and coding method
US9324331B2 (en) * 2011-01-14 2016-04-26 Panasonic Intellectual Property Corporation Of America Coding device, communication processing device, and coding method
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